In this article, we will talk briefly about data quality in SQL Server. Then, we will give a brief overview of the Melissa Data Quality for SQL Server Integration Services (SSIS), and we will demonstrate the components available in the community edition.
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In this article, I am going to explain how to use the Power BI REST API to programmatically access the Power BI Service features. I’ll also discuss the basis of REST API and how to use the APIs using Postman. Using the Power BI REST API, you can programmatically manage the administrative services like getting a list of dashboards and workspaces, refreshing a dataset, copying a dashboard from one workspace to another, and so on.
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We can use an SSIS package to perform various tasks such as data import, transform, and get output in various formats. Once we launch Visual Studio 2019 and create an integration services project, it shows you various tasks in the SSIS toolbox. In this article, we will explore the XML Task in the SSIS.
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In this article, we will be discussing how Text Mining can be done in SQL Server. For text mining in SQL Server, we will be using Integration Services (SSIS) and SQL Server Analysis Services (SSAS). This is the last article of the Data Mining series during which we discussed Naïve Bayes, Decision Trees, Time Series, Association Rules, Clustering, Linear Regression, Neural Network, Sequence Clustering. Additionally, we discussed the way to measure the accuracy of the data mining models. In the last article, we discussed how models can be extracted from the Data query.
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In our previously published articles in this series, we talked about many SSIS Hadoop components added in SQL Server 2016, such as the Hadoop connection manager, Hadoop file system task, HDFS file source, and HDFS file destination.
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The Report View in Power BI can be used to create beautiful visualizations in Power BI.
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Microsoft Power BI supports two different languages, M language and DAX (Data Analysis Expression) that can be used to filter, manage, and visualize data.
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In this article, we will be discussing how SQL Server Integration Services (SSIS) can be used to predict data mining models built from SSAS. In this article, we will be looking at the Data Mining Query in SSIS. During the data mining article series, we have discussed all the Data mining techniques that are available in SQL Server. The discussed techniques were Naïve Bayes, Decision Trees, Time Series, Association Rules, Clustering, Linear Regression, Neural Network, Sequence Clustering. Further, we discussed how the accuracy of the data mining models can be verified.
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In the previously published article, we talked briefly about Hadoop, and we gave an overview of the SSIS Hadoop components added in the SQL Server 2016 release, and we focused on the Hadoop connection manager and the Hadoop file system task.
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In this article, we will give a brief introduction of Hadoop and how it is integrated with SQL Server. Then, we will illustrate how to connect to the Hadoop cluster on-premises using the SSIS Hadoop connection manager and the related tasks.
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In this article, we will be discussing measuring Accuracy in Data Mining in SQL Server. We have discussed all the Data mining techniques that are available in SQL Server in a series of articles. The discussed techniques were Naïve Bayes, Decision Trees, Time Series, Association Rules, Clustering, Linear Regression, Neural Network, Sequence Clustering. Data mining is a predicting technique using the existing pattern. It is obvious that we won’t be able to predict 100% accurately. However, since we are using data mining outcomes for better business decisions, the result should have better accuracy. If the accuracy is very low, we tend not to use those data mining models. Therefore, it is essential to find out how accurate your data mining models are.
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In this article, we will be discussing Microsoft Sequence Clustering in SQL Server. This is the ninth article of our SQL Server Data mining techniques series. Naïve Bayes, Decision Trees, Time Series, Association Rules, Clustering, Linear Regression, Neural Network are the other techniques that we discussed until this article.
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This article explains what the basic features and differences between ETL and ELT are. I’m also going to explain in detail what an ELT pipeline is and a relevant architecture for the same in Azure. So far, we have come a long way dealing with ETL tools which basically are Extract, Transformation and Load technique used in populating a data warehouse. ELT, on the other hand, is another way to load data into a warehouse that implements the process of Extract, Load and Transform.
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In this article, I’m going to talk in detail about the moving average in SQL and how to find out the same in Power BI and Excel. This might be a new topic for many newbies who have started out in this field, but I’m sure this will help anyone who is trying to work on smoothing averages in SQL or in Power BI.
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Analyzing data with Power BI has always been fascinating to me. With ever-growing data comes more risk, and hence more security is needed. In this article, I am going to introduce what row-level security in Power BI is and how does it work. I’ll also demonstrate in detail how to implement the same in Power BI Desktop. Later, we will also discuss how to implement this row-level security in Power BI service as well. To learn about row-level security in SQL Server, please have a look at my previous article, where I have explained how to implement the same.
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In this article, we will be discussing Microsoft Neural Network in SQL Server. This is the seventh article of our SQL Server Data mining techniques series. Naïve Bayes, Decision Trees, Time Series, Association Rules, Clustering, and Linear Regression are the other techniques that we discussed until this article.
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This article explores the Merge Join Transformation in SSIS packages.
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This article helps you to understand the usage of the Fuzzy Lookup Transformation in SQL Server Integration Services (SSIS).
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In this article, I am going to give an overview of the most widely used chart types in Power BI. Power BI, as everyone might be aware, is one of the SaaS offerings from Microsoft in the field of BI and Analytics. It provides a very intuitive way for non-technical users to do analyses and create reports on their own without having to rely on a technical team. One of the main purposes of Microsoft behind Power BI is to promote self-service BI and it is one of the BI tools in the market with a comparatively lower learning curve as there are many similarities compared to Microsoft Excel.
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In this article, I’m going to discuss the different ways in which we can deploy SSAS cubes. SSAS or SQL Server Analysis Services is an enterprise-grade analytical data engine which helps business to create semantic models that can be used for reporting and making better decisions. The semantic layer created by SSAS can be accessed by client tools like Power BI, or Excel and reports can be prepared from thereon. SSAS comes with a few different versions, which are as follows.
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This article gives you an overview of representing coronavirus spread using Power BI Desktop visualizations.
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In the article Web URL configuration in a Power BI Desktop report, we explored a different method to use Web URL in a Power BI report. Images help to improve the visual appearance of a report. You can convey messages easily using these images in a report.
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In this article, I’m going to list out some of the most common warnings we encounter while developing SSAS Cubes and the reason for those warnings. As you are aware, SSAS cubes are developed using the SQL Server Data Tools (SSDT) for Analysis Services (Visual Studio), all the examples will be taken from Visual Studio 2017 only.
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DTExec is a tool used to configure and execute SQL Server Integration Services (SSIS) packages. Many developers and database administrators use this tool, especially when automating the package execution using third-party applications or non-supported programming languages.
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In the article, Candlestick chart for stock data analysis in Power BI Desktop, we explored Power BI Desktop to analyze stock prices. If you follow the stock market, you might have noticed a ticker (similar to the following image) showcasing stock prices and changes since the last close price. It shows an up and down indicator depending upon the positive or negative change in the stock price.
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